---
title: "COVID-19 | Tennessee"
output:
    flexdashboard::flex_dashboard:
      orientation: rows
      vertical_layout: scroll
      social: menu
      source_code: embed
knit: (function(input_file, encoding) {
  out_dir <- 'docs';
  rmarkdown::render(input_file,
 encoding=encoding,
 output_file=file.path(dirname(input_file), out_dir, 'index.html'))})
---
  

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(ggplot2)
library(plotly)
library(tidyverse)
library(dplyr)

#Acquire Data####
#Load NY Times Github data###
nyt_path = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'

counties = read_csv(url(nyt_path)) #Contains all counties in US.

#Separate State
tn = counties[ which(counties$state =='Tennessee'),]
tn = tn[which(tn$date < '2020-03-31'),] #The Tennessee data from the new source has data starting March 31

#Tennessee data from TN State Data Center
tn_state = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/32b104abc5d841ca895de7f7c17fc4dc/data'

download.file(tn_state,'TN_COVID19_CountyDaily.xlsx') 

#Data cleaning and processing####
tn_daily =  readxl::read_excel('TN_COVID19_CountyDaily.xlsx',sheet=1) %>%
  filter(DATE > '2020-03-30') %>%
  select(DATE, COUNTY, TEST_POS, TEST_NEG, DEATHS_TOT) %>%
  filter(COUNTY != 'Balance') 

names(tn_daily) = c('Date', 'County', 'Positive', 'Negative', 'Death')

tn_daily$County = ifelse(tn_daily$County == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'Dekalb', 
                        'DeKalb', 
                        tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'VanBuren', 
                        'Van Buren', 
                        tn_daily$County)

tn_daily$County = as.factor(tn_daily$County)

#Merge NYT and Tn Daily dataframes####
tn_daily2 = tn_daily[,c('Date','County', 'Positive', 'Death')]
names(tn_daily2) = c('date','county', 'cases', 'deaths')
tn_daily2 = tn_daily2[!(tn_daily2$county =='Out of TN' | tn_daily2$county =='Pending'),]
tn_daily2 = tibble::add_column(tn_daily2, state = 'Tennessee', .after='county')

fips_daily =tn %>% group_by(county, fips) %>% tally()

tn_daily2 = left_join(tn_daily2, fips_daily[,1:2], by ='county')
##Row bind tn_daily (TN Health Dept) with tn
tn = rbind(tn, tn_daily2) #Rbind will automatically put the correct columns together. 


#Add population####
#Get Census Population for counties in Tennessee

uscensus = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/county_pop_2019.csv'
tn_pop = read_csv(url(uscensus))
tn_pop = tn_pop[ which(tn_pop$State =='Tennessee'),]
tn_pop = tn_pop[-1,c(2:3)]
tn_pop$County = gsub(' County', '', tn_pop$County)
tn_pop$Population = as.numeric(tn_pop$Population)
tn_pop = tn_pop[, c('County', 'Population')]
names(tn_pop) = c('county', 'population')

##Combine tn (NYT) dataframe with Population
tn = left_join(tn, tn_pop, by='county')
tn$county = as.factor(tn$county)

#Calculate per million
tn['cases_per_million'] = (tn$cases/tn$population)*10^6
#Tn dataframe is ready for long term data visualiations and includes standardization by population.


#Keep most recent for tn_daily
tn_daily = tn_daily %>% group_by(County) %>% top_n(1, Date)

#Clean the global environment###
rm(list=ls()[!ls() %in% c('tn', 'tn_daily')])



#Value Box Calculations####
tn_ext =  readxl::read_excel('TN_COVID19_COUNTYDaily.xlsx',sheet=1) %>%
  top_n(1,DATE) %>%
  select(DATE:RATE_CHG_1DAY,RECOV_TOT:ACTIVE_NEW) %>%
  filter(COUNTY != 'Balance') 

tn_ext$COUNTY = ifelse(tn_ext$COUNTY == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_ext$COUNTY)


tn_ext$COUNTY = as.factor(tn_ext$COUNTY)

#Total Cases

total_cases = sum(tn_ext$TEST_POS)
total_negative = sum(tn_ext$TEST_NEG)
total_death = sum(tn_ext$DEATHS_TOT)

total_recov = sum(tn_ext$RECOV_TOT)
active_cases = total_cases - total_death - total_recov #sum(tn_ext$ACTIVE_TOT)

total_tests = total_cases + total_negative



```

Data Visualizations by County
=======================================

Rows {data-width = 150}
-----------
### Confirmed Cases to Date

```{r}
#Total Positive Cases
cases_per = ((total_cases/total_tests)*100) %>% 
  round(1) %>% 
  paste0('%')

total_cases_vb = total_cases %>% 
  formattable::comma(digits=0) %>% 
  paste0(' (',cases_per,')') 

valueBox(value = total_cases_vb, icon='fa-user-plus', color='#002D65')
```

### Negative Tests 

```{r} 
#Total Negative Cases
negative_per = ((total_negative/total_tests)*100) %>% 
  round(1) %>% paste0('%')

total_negative_vb = total_negative %>% 
  formattable::comma(digits=0) %>% paste0(' (', negative_per,')') 

valueBox(value = total_negative_vb, icon='fa-user-minus', color='#CC0000')
```


Rows {data-width = 150}
-----------

### Recovered Cases: `r total_recov %>% formattable::comma(digits=0)`
```{r}
recov_per = ((total_recov/(total_cases))*100) %>% round(1)

gauge(recov_per, min=0, max = 100, symbol = '%')
```

### Active Cases: `r active_cases %>% formattable::comma(digits=0)`
```{r}
active_per = ((active_cases/(total_cases))*100) %>% round(1) 

gauge(active_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,25), warning = c(26,100)))
```

### Total Deaths: `r total_death %>% formattable::comma(digits=0)` 

```{r} 
 #Total Deaths Cases
death_per = ((total_death/total_cases)*100) %>% round(1) %>% paste0('%')

gauge(death_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,5), warning = c(6,100)))
```


Column {data-width=650}
-----------------------------------------------------------------------

### Cases across time in most populous counties

```{r}

tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]


t_line = tn_pop_line =ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '2 day', date_labels = '%b %d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), panel.background = element_blank(), axis.line.x=element_line(), axis.line.y.left = element_line(), axis.text=element_text(face='bold'),axis.text.x = element_text(angle=45, hjust=1))+
  scale_color_brewer(palette = 'Spectral',direction=-1)
ggplotly(t_line)
```

### Cases across time in most populous counties {.mobile}

```{r}
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]

ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%m-%d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(),
        axis.line.x=element_line(), 
        axis.line.y.left = element_line(), 
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1),
        legend.position = c(0,.9),
        legend.justification = c(-0.1,.8))+
  scale_color_brewer(palette = 'Spectral',direction=-1)
```

Row {data-width=400}
-------------------------
### Cases rate 
```{r}
library(usmap)
library(viridis)
tn_geo =tn %>% group_by(county) %>% top_n(1,date)
tn_geo = tn_geo[!(tn_geo$county =='Unknown'),]
tn_geo$fips =fips(state = 'TN', county=tn_geo$county)

library(rjson)
url = 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
json_file <- rjson::fromJSON(file=url)

#Create map

fig <- plot_ly() %>% 
  add_trace(
    text = paste(tn_geo$county,' County'),
    hoverinfo = 'text',
    type='choroplethmapbox',
    geojson= json_file,
    locations=tn_geo$fips,
    z = tn_geo$cases_per_million,
    zmin=0,
    zmax = round(max(tn_geo$cases_per_million),-3),
    colorscale='Viridis',
    marker=list(line=list(
      width=0),
      opacity=0.9)) %>% 
  layout(mapbox=list(
    style="carto-positron",
    zoom =5.05,
    center=list(lon= -86.7816, lat=36.1627))) %>%
  colorbar(title = "Cases per million") 
fig

```

### Case numbers by county
```{r}
tn_daily[,2:5] %>%
  DT::datatable(rownames = FALSE,
                colnames = c('County', 'Confirmed', 'Negative', 'Death'),
                options = list(pageLength = 10))
```

Column {data-width=350, data-height=400}
-----------------------------------------------------------------------

### Positive cases by counties with more than 20 cases

```{r}
tn_cases = tn_daily[which(tn_daily$Positive >20 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), 
                    c('County', 'Positive','Negative','Death')] 
plot_ly(data=tn_cases,
        x=tn_cases$Positive,
        y=reorder(tn_cases$County, tn_cases$Positive),
        type='bar',
        orientation='h', 
        marker= list(color='#002D65')) %>%
  layout(xaxis = list(title= 'Count', 
                      zeroline = FALSE, 
                      showline = F, 
                      showticklabels = T, 
                      showgrid = T),
         yaxis = list(showgrid = FALSE, 
                      showline = FALSE, 
                      showticklabels = TRUE,
                      dtick=1,
                      tickfont = list(size=10)))
```

### Positive cases by county {.mobile}

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 20 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), 
                    c('County', 'Positive','Negative','Death')]

ggplot(data=tn_cases,aes(x=Positive, y=reorder(County,Positive)))+
  geom_col(fill='#002D65')+
  ylab('')+
  xlab('')+
  theme(panel.background = element_blank(),
        axis.line.x=element_line(), 
        axis.line.y.left = element_line(), 
        axis.text=element_text(face='bold'), 
        axis.ticks = element_blank())+
  scale_x_continuous(expand= c(0,0))+
  ggtitle("Counties with more than 20 cases")
```


### All outcomes by counties with more than 50 cases

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] #Remove where there are no cases

plot_ly(data=tn_cases,
        x= reorder(tn_cases$County, tn_cases$Negative),
        y=tn_cases$Negative,
        type='bar',
        name='Negative Cases',
        marker= list(color='grey')) %>%
          add_trace(y = tn_cases$Positive,
                    name='Positive Cases',
                    marker = list(color='#002D65')) %>%
          add_trace(y = tn_cases$Death,
                    name='Deaths',
                    marker = list(color='#CC0000')) %>%
          layout(barmode = 'stack',
                 xaxis = list(showgrid = FALSE, 
                              showlilnee = FALSE, 
                              showticklabels = TRUE,
                              dtick=1,
                              tickfont =list(size=10)),
                 yaxis = list(title= 'Count', 
                              zeroline = FALSE, 
                              showline = F, 
                              showticklabels = T, 
                              showgrid = T),
                 hovermode = 'compare')
```

### All Cases by County {.mobile}

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), 
                    c('County', 'Death','Negative','Positive')] %>%
  gather(Cases, Count, Death:Positive) %>% 
  mutate(Cases = factor(Cases, levels = c("Death", "Positive", "Negative")))

ggplot(tn_cases,aes(x=reorder(County, -Count, sum), y= Count, fill = Cases))+
  geom_bar(position='stack', stat =  'identity')+
  xlab('')+
  theme(panel.background = element_blank(), 
              axis.line = element_line(), 
              axis.text = element_text(face = 'bold'),
              axis.text.x = element_text(angle = 30, h=1),
              legend.title = element_blank(), 
              legend.direction='horizontal',
              legend.position = c(.85,.90),
              legend.box.just = 'left')+
  scale_fill_manual(values = c(Death = '#CC0000', Positive = '#002D65', 'Negative' = 'grey')) +
  scale_y_continuous(labels = scales::comma,breaks = seq(min(tn_cases$Count), max(tn_cases$Count)*1.5, by=2000))
```

Data Visualizatons by Demographics
==================================

Column {data-width=350, data-height=450}
---------------------------
### Confirmed Cases by Age
```{r}
#Get US Census Demographic Data 
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

age_census = readxl::read_excel('census_demographics.xlsx',sheet='Age') %>% 
  select(Age, Percent)%>%
  rename('Census_Percent' = 'Percent') 

#Get TN Cases Data
tn_age = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/1bdfe86c38514c9c878241d5230d9a85/data'

download.file(tn_age,'TN_Age.xlsx') 

tn_age =  readxl::read_excel('TN_Age.xlsx',sheet=1) %>% 
  top_n(1,DATE) %>%
  select(DATE, AGE, TOT_CASE_COUNT, DEATHS_TOT)

names(tn_age) = c('Date', 'Age', 'Count',  'Deaths')

tn_age$Age = as.factor(tn_age$Age)
tn_age$Case_Percent = (tn_age$Count/sum(tn_age$Count))*100
tn_age$Death_Percent =(tn_age$Deaths/sum(tn_age$Deaths))*100

tn_age = cbind(tn_age[,c('Age', 'Case_Percent','Death_Percent')], age_census[,2])
tn_age$Census_Percent[10] = NA

tn_age = tidyr::gather(tn_age,'Percent', 'Value', -Age)

fills = c('Case_Percent' = '#002D65', 'Death_Percent' = '#CC0000', 'Census_Percent' = 'grey')

ggplot(tn_age,aes(x=Age))+
  geom_col(aes(y = Value, fill=Percent),position=position_dodge())+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30, h=1),
        legend.title = element_blank(),
        legend.position = c(.2,.90),
        legend.box.just = 'left')+
  scale_fill_manual(values= fills, labels=c('Percent Cases', 'Percent Population', 'Percent Deaths'))

```

### Confirmed Cases by Sex
```{r}
#Get US Census Data
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

sex_census = readxl::read_excel('census_demographics.xlsx',sheet='Sex') %>% 
  rename('Census_Percent' = 'Percent')

#Get TN Cases Data
tn_demo = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/4ff6b762d64a4e0caa626df00a76c902/data'

download.file(tn_demo,'TN_Demographics.xlsx') 

tn_demo =  readxl::read_excel('TN_Demographics.xlsx',sheet=1) %>% 
  top_n(1,DATE) %>%
  select(DATE, TYPE, DETAIL, TOT_CASE_COUNT) %>%
  group_split(TYPE)

tn_sex = tn_demo[[2]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_sex) = c('Sex', 'Count')
tn_sex$Case_Percent = (tn_sex$Count/sum(tn_sex$Count))*100

tn_sex = dplyr::left_join(tn_sex[,c(1,3)], sex_census, 'Sex') %>%
  gather('Percent', 'Value', -Sex)


fills = c('Case_Percent' = '#002D65', 'Census_Percent' = 'grey')

ggplot(data=tn_sex, aes(x=Sex))+
  geom_col(aes(y=Value,fill=Percent), position=position_dodge())+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        legend.title = element_blank(), 
        legend.position = c(.85,.90),
        legend.box.just = 'left')+
  scale_fill_manual(values= fills, labels=c('Percent Cases', 'Percent Population'))
```



Column {data-width=350, data-height=450}
---------------------------

### Confirmed Cases by Race
```{r}
#Get US Census Data
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

race_census = readxl::read_excel('census_demographics.xlsx',sheet='Race') %>% 
  select(Race = Race, Census_Percent =Percent)

#Get TN Data
tn_race = tn_demo[[3]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_race) = c('Race', 'Count')
tn_race$Case_Percent = (tn_race$Count/sum(tn_race$Count))*100

tn_race = dplyr::left_join(tn_race[,c(1,3)], race_census, 'Race')
tn_race$Race = factor(tn_race$Race, levels = c('Asian', 'Black or African American', 'White', 'Other/Two or More Races', 'Pending'))
tn_race = tidyr::gather(tn_race, 'Percent', 'Value', -Race)

fills = c('Case_Percent' = '#002D65', 'Census_Percent' = 'grey')

ggplot(data=tn_race, aes(x=Race))+
  geom_col(aes(y=Value, fill=Percent), position=position_dodge())+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30, h = 1),
        legend.title = element_blank(), 
        legend.position = c(.85,.90),
        legend.box.just = 'left')+
  scale_fill_manual(values= fills, labels=c('Percent Cases', 'Percent Population'))
```

### Confirmed Cases by Ethnicity
```{r}
#Get US Census Data
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

eth_census = readxl::read_excel('census_demographics.xlsx',sheet='Ethnicity') %>% select(Ethnicity=Ethnicity,Census_Percent =Percent)

#Get Tn Case data
tn_eth = tn_demo[[1]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_eth) = c('Ethnicity', 'Count')
tn_eth$Case_Percent = tn_eth$Count/sum(tn_eth$Count)*100

tn_eth = dplyr::left_join(tn_eth[,c(1,3)], eth_census, 'Ethnicity')
tn_eth$Ethnicity = factor(tn_eth$Ethnicity, levels = c('Hispanic or Latino','Not Hispanic or Latino', 'Pending'))
tn_eth = tidyr::gather(tn_eth, 'Percent', 'Value', -Ethnicity)

fills = c('Case_Percent' = '#002D65', 'Census_Percent' = 'grey')
ggplot(data=tn_eth, aes(x=Ethnicity))+
  geom_col(aes(y=Value, fill=Percent), position = position_dodge())+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30, h = 1),
        legend.title = element_blank(), 
        legend.position = c(.85,.90),
        legend.box.just = 'left')+
  scale_fill_manual(values= fills, labels=c('Percent Cases', 'Percent Population'))
```


About 
================================

**The Tennessee Coronavirus Dashboard**    
  
The sole intention of this Coronavirus dashboard is to provide a visual overview of the 2019 Novel COVID-19 as it relates to counties in Tennessee. The data is acquired from two different sources, and there are no guarantees on the accuracy of the data becaues of differences in numbers reported and reporting time.   

Note: This dashboard has different graphs for small screens. For more interactive graphs, please view this website on a large screen (computer/large table).   


**Data**

Data for "Cases across time in most populous counties" is a concatenation of the [New York Times Coronavirus Data](https://github.com/nytimes/covid-19-data) and the [Tennessee State Data Center](https://myutk.maps.arcgis.com/home/group.html?id=c98fc99308dd43fb98146d3cf21fc31c&q=tags%3A%22COVID-19%22&view=list&focus=files#content), which acquires its data from the [TN Department of Health](https://www.tn.gov/health/cedep/ncov.html)

Latest data from `r max(tn$date) %>% format('%m-%d')`.

Population data acquired from the [US Census](https://data.census.gov/cedsci/table?q=Tennessee%20race%20demographics&g=0400000US47&tid=ACSDP1Y2018.DP05&hidePreview=true).

Created by [Malle Carrasco-Harris](https://www.linkedin.com/in/malle-carrasco-harris).